CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

Novelty detection, i.e., identifying whether a given sample is drawn from outside the training distribution, is essential for reliable machine learning. To this end, there have been many attempts at learning a representation well-suited for novelty detection and designing a score based on such representation... (read more)

PDF Abstract NeurIPS 2020 PDF NeurIPS 2020 Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Anomaly Detection One-class CIFAR-10 CSI AUROC 94.3 # 1
Anomaly Detection One-class CIFAR-100 CSI AUROC 89.6 # 1
Anomaly Detection One-class ImageNet-30 CSI AUROC 91.6 # 1
Anomaly Detection Unlabeled CIFAR-10 vs CIFAR-100 CSI AUROC 89.3 # 1

Methods used in the Paper


METHOD TYPE
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